In [1]:
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
In [2]:
gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
print('Not connected to a GPU')
else:
print(gpu_info)
Wed Dec 21 10:08:15 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |
| N/A 44C P0 26W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
In [3]:
from psutil import virtual_memory
ram_gb = virtual_memory().total / 1e9
print('Your runtime has {:.1f} gigabytes of available RAM\n'.format(ram_gb))
if ram_gb < 20:
print('Not using a high-RAM runtime')
else:
print('You are using a high-RAM runtime!')
Your runtime has 27.3 gigabytes of available RAM You are using a high-RAM runtime!
In [ ]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
pass
# for filename in filenames:
# print(os.path.join(dirname, filename))
# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
Download yoloV5¶
In [4]:
!pip install kaggle
from google.colab import files
files.upload()
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: kaggle in /usr/local/lib/python3.8/dist-packages (1.5.12) Requirement already satisfied: urllib3 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.24.3) Requirement already satisfied: certifi in /usr/local/lib/python3.8/dist-packages (from kaggle) (2022.12.7) Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.23.0) Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.8.2) Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.15.0) Requirement already satisfied: python-slugify in /usr/local/lib/python3.8/dist-packages (from kaggle) (7.0.0) Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from kaggle) (4.64.1) Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.8/dist-packages (from python-slugify->kaggle) (1.3) Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (3.0.4) Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (2.10)
Saving kaggle.json to kaggle.json
Out[4]:
{'kaggle.json': b'{"username":"jabullae","key":"9615fe6e84855ac0d22aa288ab5d10bd"}'}
In [5]:
!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
In [6]:
! kaggle datasets download -d kneroma/tacotrashdataset
Downloading tacotrashdataset.zip to /content 99% 2.77G/2.79G [00:18<00:00, 216MB/s] 100% 2.79G/2.79G [00:18<00:00, 161MB/s]
In [11]:
! unzip /content/tacotrashdataset.zip
Archive: /content/tacotrashdataset.zip inflating: best-checkpoint-003epoch.bin inflating: data/annotations.json inflating: data/batch_1/000000.jpg inflating: data/batch_1/000001.jpg inflating: data/batch_1/000003.jpg inflating: data/batch_1/000004.jpg inflating: data/batch_1/000005.jpg inflating: data/batch_1/000006.jpg inflating: data/batch_1/000007.jpg inflating: data/batch_1/000008.jpg inflating: data/batch_1/000010.jpg inflating: data/batch_1/000011.jpg inflating: data/batch_1/000012.jpg inflating: data/batch_1/000013.jpg inflating: data/batch_1/000014.jpg inflating: data/batch_1/000015.jpg inflating: data/batch_1/000016.jpg inflating: data/batch_1/000017.jpg inflating: data/batch_1/000019.jpg inflating: data/batch_1/000021.jpg inflating: data/batch_1/000022.jpg inflating: data/batch_1/000023.jpg inflating: data/batch_1/000024.jpg inflating: data/batch_1/000025.jpg inflating: data/batch_1/000026.jpg inflating: data/batch_1/000027.jpg inflating: data/batch_1/000028.jpg inflating: 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In [13]:
%cd /content/drive/MyDrive/MiniProject
/content/drive/MyDrive/MiniProject
In [14]:
!ls
best-checkpoint-003epoch.bin kaggle.json tacotrashdataset.zip data kle_log.txt '딥러닝 미니프로젝트.ipynb' meta_df.csv
In [16]:
!git clone https://github.com/ultralytics/yolov5 # clone
%cd /content/drive/MyDrive/MiniProject/yolov5
%pip install -qr requirements.txt # install
# %pip install torch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0
import torch
from yolov5 import utils
display = utils.notebook_init() # checks
print (torch.__version__)
YOLOv5 🚀 v7.0-46-g96a71b1 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)
Setup complete ✅ (4 CPUs, 25.5 GB RAM, 28.1/166.8 GB disk) 1.13.0+cu116
In [17]:
!ls
benchmarks.py data LICENSE requirements.txt tutorial.ipynb CITATION.cff detect.py models segment utils classify export.py README.md setup.cfg val.py CONTRIBUTING.md hubconf.py README.zh-CN.md train.py yolov5
In [18]:
# !pip uninstall typing -y
!pip install pycocotools
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: pycocotools in /usr/local/lib/python3.8/dist-packages (2.0.6) Requirement already satisfied: matplotlib>=2.1.0 in /usr/local/lib/python3.8/dist-packages (from pycocotools) (3.2.2) Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from pycocotools) (1.21.6) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.1.0->pycocotools) (1.4.4) Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.1.0->pycocotools) (2.8.2) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.1.0->pycocotools) (0.11.0) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.1.0->pycocotools) (3.0.9) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib>=2.1.0->pycocotools) (1.15.0)
In [19]:
!pip install torch torchvision torchaudio
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.0+cu116) Requirement already satisfied: torchvision in /usr/local/lib/python3.8/dist-packages (0.14.0+cu116) Requirement already satisfied: torchaudio in /usr/local/lib/python3.8/dist-packages (0.13.0+cu116) Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.4.0) Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from torchvision) (2.23.0) Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from torchvision) (1.21.6) Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.8/dist-packages (from torchvision) (7.1.2) Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (3.0.4) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (2022.12.7) Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (2.10) Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (1.24.3)
In [20]:
import os
import shutil
import numpy as np
import tqdm
In [21]:
!ls
benchmarks.py data LICENSE requirements.txt tutorial.ipynb CITATION.cff detect.py models segment utils classify export.py README.md setup.cfg val.py CONTRIBUTING.md hubconf.py README.zh-CN.md train.py yolov5
Read the annotations file
In [25]:
%cd /content/drive/MyDrive/MiniProject
/content/drive/MyDrive/MiniProject
In [27]:
from pycocotools.coco import COCO
data_source = COCO(annotation_file='data/annotations.json')
loading annotations into memory... Done (t=0.34s) creating index... index created!
In [28]:
catIds = data_source.getCatIds()
cats = data_source.loadCats(catIds)
In [29]:
cats
Out[29]:
[{'supercategory': 'Aluminium foil', 'id': 0, 'name': 'Aluminium foil'},
{'supercategory': 'Battery', 'id': 1, 'name': 'Battery'},
{'supercategory': 'Blister pack', 'id': 2, 'name': 'Aluminium blister pack'},
{'supercategory': 'Blister pack', 'id': 3, 'name': 'Carded blister pack'},
{'supercategory': 'Bottle', 'id': 4, 'name': 'Other plastic bottle'},
{'supercategory': 'Bottle', 'id': 5, 'name': 'Clear plastic bottle'},
{'supercategory': 'Bottle', 'id': 6, 'name': 'Glass bottle'},
{'supercategory': 'Bottle cap', 'id': 7, 'name': 'Plastic bottle cap'},
{'supercategory': 'Bottle cap', 'id': 8, 'name': 'Metal bottle cap'},
{'supercategory': 'Broken glass', 'id': 9, 'name': 'Broken glass'},
{'supercategory': 'Can', 'id': 10, 'name': 'Food Can'},
{'supercategory': 'Can', 'id': 11, 'name': 'Aerosol'},
{'supercategory': 'Can', 'id': 12, 'name': 'Drink can'},
{'supercategory': 'Carton', 'id': 13, 'name': 'Toilet tube'},
{'supercategory': 'Carton', 'id': 14, 'name': 'Other carton'},
{'supercategory': 'Carton', 'id': 15, 'name': 'Egg carton'},
{'supercategory': 'Carton', 'id': 16, 'name': 'Drink carton'},
{'supercategory': 'Carton', 'id': 17, 'name': 'Corrugated carton'},
{'supercategory': 'Carton', 'id': 18, 'name': 'Meal carton'},
{'supercategory': 'Carton', 'id': 19, 'name': 'Pizza box'},
{'supercategory': 'Cup', 'id': 20, 'name': 'Paper cup'},
{'supercategory': 'Cup', 'id': 21, 'name': 'Disposable plastic cup'},
{'supercategory': 'Cup', 'id': 22, 'name': 'Foam cup'},
{'supercategory': 'Cup', 'id': 23, 'name': 'Glass cup'},
{'supercategory': 'Cup', 'id': 24, 'name': 'Other plastic cup'},
{'supercategory': 'Food waste', 'id': 25, 'name': 'Food waste'},
{'supercategory': 'Glass jar', 'id': 26, 'name': 'Glass jar'},
{'supercategory': 'Lid', 'id': 27, 'name': 'Plastic lid'},
{'supercategory': 'Lid', 'id': 28, 'name': 'Metal lid'},
{'supercategory': 'Other plastic', 'id': 29, 'name': 'Other plastic'},
{'supercategory': 'Paper', 'id': 30, 'name': 'Magazine paper'},
{'supercategory': 'Paper', 'id': 31, 'name': 'Tissues'},
{'supercategory': 'Paper', 'id': 32, 'name': 'Wrapping paper'},
{'supercategory': 'Paper', 'id': 33, 'name': 'Normal paper'},
{'supercategory': 'Paper bag', 'id': 34, 'name': 'Paper bag'},
{'supercategory': 'Paper bag', 'id': 35, 'name': 'Plastified paper bag'},
{'supercategory': 'Plastic bag & wrapper', 'id': 36, 'name': 'Plastic film'},
{'supercategory': 'Plastic bag & wrapper',
'id': 37,
'name': 'Six pack rings'},
{'supercategory': 'Plastic bag & wrapper', 'id': 38, 'name': 'Garbage bag'},
{'supercategory': 'Plastic bag & wrapper',
'id': 39,
'name': 'Other plastic wrapper'},
{'supercategory': 'Plastic bag & wrapper',
'id': 40,
'name': 'Single-use carrier bag'},
{'supercategory': 'Plastic bag & wrapper',
'id': 41,
'name': 'Polypropylene bag'},
{'supercategory': 'Plastic bag & wrapper', 'id': 42, 'name': 'Crisp packet'},
{'supercategory': 'Plastic container', 'id': 43, 'name': 'Spread tub'},
{'supercategory': 'Plastic container', 'id': 44, 'name': 'Tupperware'},
{'supercategory': 'Plastic container',
'id': 45,
'name': 'Disposable food container'},
{'supercategory': 'Plastic container',
'id': 46,
'name': 'Foam food container'},
{'supercategory': 'Plastic container',
'id': 47,
'name': 'Other plastic container'},
{'supercategory': 'Plastic glooves', 'id': 48, 'name': 'Plastic glooves'},
{'supercategory': 'Plastic utensils', 'id': 49, 'name': 'Plastic utensils'},
{'supercategory': 'Pop tab', 'id': 50, 'name': 'Pop tab'},
{'supercategory': 'Rope & strings', 'id': 51, 'name': 'Rope & strings'},
{'supercategory': 'Scrap metal', 'id': 52, 'name': 'Scrap metal'},
{'supercategory': 'Shoe', 'id': 53, 'name': 'Shoe'},
{'supercategory': 'Squeezable tube', 'id': 54, 'name': 'Squeezable tube'},
{'supercategory': 'Straw', 'id': 55, 'name': 'Plastic straw'},
{'supercategory': 'Straw', 'id': 56, 'name': 'Paper straw'},
{'supercategory': 'Styrofoam piece', 'id': 57, 'name': 'Styrofoam piece'},
{'supercategory': 'Unlabeled litter', 'id': 58, 'name': 'Unlabeled litter'},
{'supercategory': 'Cigarette', 'id': 59, 'name': 'Cigarette'}]
In [30]:
trash = ['Cigarette', 'Unlabeled litter', 'Other plastic wrapper', 'Plastic straw', 'Aluminium foil', 'Tissues',
'Rope & strings', 'Aluminium blister pack', 'Paper straw', 'Plastic glooves', 'Shoe']
plastic = ['Plastic film', 'Clear plastic bottle', 'Other plastic', 'Plastic bottle cap', 'Disposable plastic cup',
'Spread tub', 'Other plastic bottle', 'Plastic lid', 'Disposable food container', 'Other plastic cup',
'Carded blister pack', 'Plastic utensils', 'Squeezable tube', 'Other plastic container', 'Six pack rings',
'Tupperware', 'Polypropylene bag']
metal = ['Drink can', 'Pop tab', 'Metal bottle cap', 'Food Can', 'Aerosol', 'Metal lid', 'Scrap metal']
glass = ['Broken glass', 'Glass bottle', 'Glass jar', 'Glass cup']
styrofoam = ['Styrofoam piece', 'Foam food container', 'Foam cup']
paper = ['Other carton', 'Plastified paper bag', 'Normal paper', 'Meal carton','Paper cup', 'Corrugated carton', 'Wrapping paper',
'Toilet tube', 'Magazine paper', 'Egg carton', 'Paper bag', 'Drink carton', 'Pizza box']
vinyl = ['Single-use carrier bag', 'Crisp packet', 'Garbage bag']
food_waste = ['food waste']
battery = ['Battery']
for i in cats:
if i['name'] in trash:
i['name'] = 'trash'
i['id'] = 0
if i['name'] in plastic:
i['name'] = 'plastic'
i['id'] = 1
if i['name'] in metal:
i['name'] = 'metal'
i['id'] = 2
if i['name'] in glass:
i['name'] = 'glass'
i['id'] = 3
if i['name'] in styrofoam:
i['name'] = 'styrofoam'
i['id'] = 4
if i['name'] in paper:
i['name'] = 'paper'
i['id'] = 5
if i['name'] in vinyl:
i['name'] = 'vinyl'
i['id'] = 6
if i['name'] in food_waste:
i['name'] = 'food waste'
i['id'] = 7
if i['name'] in battery:
i['name'] = 'battery'
i['id'] = 8
In [31]:
cats
Out[31]:
[{'supercategory': 'Aluminium foil', 'id': 0, 'name': 'trash'},
{'supercategory': 'Battery', 'id': 8, 'name': 'battery'},
{'supercategory': 'Blister pack', 'id': 0, 'name': 'trash'},
{'supercategory': 'Blister pack', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Bottle', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Bottle', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Bottle', 'id': 3, 'name': 'glass'},
{'supercategory': 'Bottle cap', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Bottle cap', 'id': 2, 'name': 'metal'},
{'supercategory': 'Broken glass', 'id': 3, 'name': 'glass'},
{'supercategory': 'Can', 'id': 2, 'name': 'metal'},
{'supercategory': 'Can', 'id': 2, 'name': 'metal'},
{'supercategory': 'Can', 'id': 2, 'name': 'metal'},
{'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
{'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
{'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
{'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
{'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
{'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
{'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
{'supercategory': 'Cup', 'id': 5, 'name': 'paper'},
{'supercategory': 'Cup', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Cup', 'id': 4, 'name': 'styrofoam'},
{'supercategory': 'Cup', 'id': 3, 'name': 'glass'},
{'supercategory': 'Cup', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Food waste', 'id': 25, 'name': 'Food waste'},
{'supercategory': 'Glass jar', 'id': 3, 'name': 'glass'},
{'supercategory': 'Lid', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Lid', 'id': 2, 'name': 'metal'},
{'supercategory': 'Other plastic', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Paper', 'id': 5, 'name': 'paper'},
{'supercategory': 'Paper', 'id': 0, 'name': 'trash'},
{'supercategory': 'Paper', 'id': 5, 'name': 'paper'},
{'supercategory': 'Paper', 'id': 5, 'name': 'paper'},
{'supercategory': 'Paper bag', 'id': 5, 'name': 'paper'},
{'supercategory': 'Paper bag', 'id': 5, 'name': 'paper'},
{'supercategory': 'Plastic bag & wrapper', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Plastic bag & wrapper', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Plastic bag & wrapper', 'id': 6, 'name': 'vinyl'},
{'supercategory': 'Plastic bag & wrapper', 'id': 0, 'name': 'trash'},
{'supercategory': 'Plastic bag & wrapper', 'id': 6, 'name': 'vinyl'},
{'supercategory': 'Plastic bag & wrapper', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Plastic bag & wrapper', 'id': 6, 'name': 'vinyl'},
{'supercategory': 'Plastic container', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Plastic container', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Plastic container', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Plastic container', 'id': 4, 'name': 'styrofoam'},
{'supercategory': 'Plastic container', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Plastic glooves', 'id': 0, 'name': 'trash'},
{'supercategory': 'Plastic utensils', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Pop tab', 'id': 2, 'name': 'metal'},
{'supercategory': 'Rope & strings', 'id': 0, 'name': 'trash'},
{'supercategory': 'Scrap metal', 'id': 2, 'name': 'metal'},
{'supercategory': 'Shoe', 'id': 0, 'name': 'trash'},
{'supercategory': 'Squeezable tube', 'id': 1, 'name': 'plastic'},
{'supercategory': 'Straw', 'id': 0, 'name': 'trash'},
{'supercategory': 'Straw', 'id': 0, 'name': 'trash'},
{'supercategory': 'Styrofoam piece', 'id': 4, 'name': 'styrofoam'},
{'supercategory': 'Unlabeled litter', 'id': 0, 'name': 'trash'},
{'supercategory': 'Cigarette', 'id': 0, 'name': 'trash'}]
Creating a class Dictionary
classes= Mapping of Class Name to ID
In [42]:
annotation
Out[42]:
{'id': 2,
'image_id': 1,
'category_id': 18,
'segmentation': [[928.0,
1876.0,
938.0,
1856.0,
968.0,
1826.0,
990.0,
1808.0,
998.0,
1790.0,
1069.0,
1727.0,
1096.0,
1702.0,
1159.0,
1644.0,
1212.0,
1588.0,
1258.0,
1540.0,
1314.0,
1482.0,
1357.0,
1444.0,
1392.0,
1416.0,
1409.0,
1393.0,
1430.0,
1369.0,
1415.0,
1347.0,
1130.0,
1087.0,
780.0,
763.0,
528.0,
533.0,
479.0,
486.0,
466.0,
466.0,
448.0,
457.0,
427.0,
468.0,
387.0,
502.0,
321.0,
554.0,
244.0,
608.0,
118.0,
693.0,
37.0,
750.0,
3.0,
780.0,
1.0,
995.0,
28.0,
1032.0,
104.0,
1119.0,
403.0,
1471.0,
666.0,
1805.0,
763.0,
1954.0,
782.0,
1945.0,
796.0,
1970.0,
803.0,
1976.0,
818.0,
1976.0,
836.0,
1956.0,
852.0,
1954.0,
860.0,
1937.0,
873.0,
1931.0,
885.0,
1908.0,
898.0,
1896.0,
928.0,
1876.0]],
'area': 1071259.5,
'bbox': [1.0, 457.0, 1429.0, 1519.0],
'iscrowd': 0}
In [43]:
classes = {'trash' : 0,
'plastic' : 1,
'metal' : 2,
'glass' : 3,
'styrofoam' : 4,
'paper' : 5,
'vinyl' : 6,
'food waste' : 7,
'battery' : 8}
taco_labels = {0:0,
1:1,
2:2,
3:3,
4:4,
5:5,
6:6,
7:7,
8:8,
}
taco_labels_inverse = {0:0,
1:8,
2:0,
3:1,
4:1,
5:1,
6:3,
7:1,
8:2,
9:3,
10:2,
11:2,
12:2,
13:5,
14:5,
15:5,
16:5,
17:5,
18:5,
19:5,
20:5,
21:1,
22:4,
23:3,
24:1,
25:7,
26:3,
27:1,
28:2,
29:1,
30:5,
31:0,
32:5,
33:5,
34:5,
35:5,
36:1,
37:1,
38:6,
39:0,
40:6,
41:1,
42:6,
43:1,
44:1,
45:1,
46:4,
47:1,
48:0,
49:1,
50:2,
51:0,
52:2,
53:0,
54:1,
55:0,
56:0,
57:4,
58:0,
59:0}
In [34]:
# classes = {}
# taco_labels = {}
# taco_labels_inverse = {}
# for c in cats:
# taco_labels[len(classes)] = c['id']
# taco_labels_inverse[c['id']] = len(classes)
# classes[c['name']] = len(classes)
Splitting data into train, val and test
In [35]:
!ls
best-checkpoint-003epoch.bin kaggle.json tacotrashdataset.zip data kle_log.txt yolov5 '딥러닝 미니프로젝트.ipynb' meta_df.csv
In [36]:
%cd /content/drive/MyDrive/MiniProject/yolov5
!mkdir -p tmp/labels tmp/images
IMAGES_PATH = 'tmp/images/'
LABELS_PATH = 'tmp/labels/'
/content/drive/MyDrive/MiniProject/yolov5
In [37]:
!ls tmp
images labels
In [38]:
import shutil
import os
In [44]:
imgIds = data_source.getImgIds()
# print(data_source.loadImgs(0)[0])
for index, img_id in tqdm.tqdm(enumerate(imgIds)):
img_info = data_source.loadImgs(img_id)[0]
# img_dir: batch_x/.....jpg ---> batch_x_......jpg
img_dir = img_info['file_name'].replace('/', '-')
image_name = img_dir.split('.')[0]
label_dir = LABELS_PATH + image_name + '.txt'
height = img_info['height']
width = img_info['width']
# print ("Copying from /kaggle/input/tacotrashdataset/data/{} to {}".format(img_info['file_name'], os.path.join(IMAGES_PATH, img_dir)))
# get images
shutil.copy(f"/content/drive/MyDrive/MiniProject/data/{img_info['file_name']}", os.path.join(IMAGES_PATH, img_dir))
# get labels
with open(label_dir, mode='w') as fp:
# print (f"Creating label_dir {label_dir} for {image_name}")
annotation_id = data_source.getAnnIds(img_id)
if len(annotation_id) == 0:
fp.write('')
continue
boxes = np.zeros((0, 5))
annotations = data_source.loadAnns(annotation_id)
lines = ''
for annotation in annotations:
label = taco_labels_inverse[annotation['category_id']]
box = annotation['bbox']
# some annotations have basically no width / height (extremely small), skip them
if box[2] < 1 or box[3] < 1:
continue
# top_x,top_y,width,height ----> cen_x,cen_y,width,height
# standardize to 0-1
box[0] = round((box[0] + box[2] / 2) / width, 6)
box[1] = round((box[1] + box[3] / 2) / height, 6)
box[2] = round(box[2] / width, 6)
box[3] = round(box[3] / height, 6)
# line: label x_center y_center width height
lines = lines + str(label)
for i in box:
lines += ' ' + str(i)
lines += '\n'
fp.writelines(lines)
1500it [06:53, 3.63it/s]
In [45]:
!ls
benchmarks.py detect.py README.md tmp yolov5 CITATION.cff export.py README.zh-CN.md train.py classify hubconf.py requirements.txt tutorial.ipynb CONTRIBUTING.md LICENSE segment utils data models setup.cfg val.py
In [46]:
# test
annotation_id = data_source.getAnnIds(1)
data_source.loadAnns(annotation_id)
Out[46]:
[{'id': 2,
'image_id': 1,
'category_id': 18,
'segmentation': [[928.0,
1876.0,
938.0,
1856.0,
968.0,
1826.0,
990.0,
1808.0,
998.0,
1790.0,
1069.0,
1727.0,
1096.0,
1702.0,
1159.0,
1644.0,
1212.0,
1588.0,
1258.0,
1540.0,
1314.0,
1482.0,
1357.0,
1444.0,
1392.0,
1416.0,
1409.0,
1393.0,
1430.0,
1369.0,
1415.0,
1347.0,
1130.0,
1087.0,
780.0,
763.0,
528.0,
533.0,
479.0,
486.0,
466.0,
466.0,
448.0,
457.0,
427.0,
468.0,
387.0,
502.0,
321.0,
554.0,
244.0,
608.0,
118.0,
693.0,
37.0,
750.0,
3.0,
780.0,
1.0,
995.0,
28.0,
1032.0,
104.0,
1119.0,
403.0,
1471.0,
666.0,
1805.0,
763.0,
1954.0,
782.0,
1945.0,
796.0,
1970.0,
803.0,
1976.0,
818.0,
1976.0,
836.0,
1956.0,
852.0,
1954.0,
860.0,
1937.0,
873.0,
1931.0,
885.0,
1908.0,
898.0,
1896.0,
928.0,
1876.0]],
'area': 1071259.5,
'bbox': [0.465517, 0.593704, 0.929733, 0.741337],
'iscrowd': 0},
{'id': 3,
'image_id': 1,
'category_id': 14,
'segmentation': [[617.0,
383.0,
703.0,
437.0,
713.0,
456.0,
725.0,
459.0,
747.0,
482.0,
760.0,
483.0,
780.0,
506.0,
794.0,
520.0,
807.0,
528.0,
827.0,
537.0,
835.0,
551.0,
852.0,
555.0,
882.0,
576.0,
913.0,
596.0,
929.0,
605.0,
954.0,
617.0,
972.0,
622.0,
998.0,
630.0,
1034.0,
640.0,
1051.0,
644.0,
1064.0,
632.0,
1081.0,
616.0,
1104.0,
589.0,
1121.0,
576.0,
1152.0,
566.0,
1177.0,
564.0,
1201.0,
569.0,
1231.0,
589.0,
1260.0,
613.0,
1277.0,
644.0,
1298.0,
669.0,
1318.0,
694.0,
1343.0,
724.0,
1362.0,
756.0,
1378.0,
779.0,
1389.0,
795.0,
1389.0,
801.0,
1398.0,
811.0,
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'area': 99583.5,
'bbox': [0.672739, 0.306491, 0.654522, 0.327965],
'iscrowd': 0}]
In [47]:
print(len(os.listdir(IMAGES_PATH)))
print(len(os.listdir(LABELS_PATH)))
1500 1500
In [48]:
!pip install split-folders
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Collecting split-folders Downloading split_folders-0.5.1-py3-none-any.whl (8.4 kB) Installing collected packages: split-folders Successfully installed split-folders-0.5.1
In [51]:
import splitfolders
%cd /content/drive/MyDrive/MiniProject/yolov5
splitfolders.ratio('tmp', output='taco', seed=1337, ratio=(.8, 0.1,0.1))
/content/drive/MyDrive/MiniProject/yolov5
Copying files: 3000 files [00:40, 74.52 files/s]
In [52]:
!ls taco
test train val
In [53]:
# check files
print(sorted(os.listdir('taco/train/images'))[:5])
print(sorted(os.listdir('taco/val/images'))[:5])
print(sorted(os.listdir('taco/test/images'))[:5])
['batch_1-000001.jpg', 'batch_1-000003.jpg', 'batch_1-000004.jpg', 'batch_1-000005.jpg', 'batch_1-000007.jpg'] ['batch_1-000000.jpg', 'batch_1-000006.jpg', 'batch_1-000016.jpg', 'batch_1-000030.jpg', 'batch_1-000038.jpg'] ['batch_1-000029.jpg', 'batch_1-000045.jpg', 'batch_1-000047.jpg', 'batch_1-000065.JPG', 'batch_1-000108.JPG']
In [54]:
#customize iPython writefile so we can write variables
from IPython.core.magic import register_line_cell_magic
@register_line_cell_magic
def writetemplate(line, cell):
print(line)
with open(line, 'w') as f:
f.write(cell.format(**globals()))
In [55]:
%cd /content/drive/MyDrive/MiniProject/yolov5
!ls
/content/drive/MyDrive/MiniProject/yolov5 benchmarks.py detect.py README.md taco val.py CITATION.cff export.py README.zh-CN.md tmp yolov5 classify hubconf.py requirements.txt train.py CONTRIBUTING.md LICENSE segment tutorial.ipynb data models setup.cfg utils
In [57]:
%%writetemplate data/taco9.yaml
train: taco/train/images
val: taco/val/images
# number of classes
nc: 9
# class names
names: ['trash',
'plastic',
'metal',
'glass',
'styrofoam',
'paper',
'vinyl',
'food waste',
'battery']
data/taco9.yaml
In [58]:
!pwd
/content/drive/MyDrive/MiniProject/yolov5
In [61]:
%cd /content/drive/MyDrive/MiniProject/yolov5
# %cd yolov5
%cat data/taco9.yaml
/content/drive/MyDrive/MiniProject/yolov5
train: taco/train/images
val: taco/val/images
# number of classes
nc: 9
# class names
names: ['trash',
'plastic',
'metal',
'glass',
'styrofoam',
'paper',
'vinyl',
'food waste',
'battery']
In [62]:
# Removing the tmp folder
!rm -r tmp
In [63]:
!wandb disabled
/bin/bash: wandb: command not found
In [64]:
!pwd
/content/drive/MyDrive/MiniProject/yolov5
In [ ]:
import time
start = time.time()
!python train.py --img 640 --batch 16 --epochs 10 --data taco9.yaml --weights yolov5s.pt
end = time.time()
train: weights=yolov5s.pt, cfg=, data=taco9.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=10, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest github: up to date with https://github.com/ultralytics/yolov5 ✅ YOLOv5 🚀 v7.0-46-g96a71b1 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB) hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0 ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt... 100% 14.1M/14.1M [00:00<00:00, 57.6MB/s] Overriding model.yaml nc=80 with nc=9 from n params module arguments 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] 2 -1 1 18816 models.common.C3 [64, 64, 1] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 2 115712 models.common.C3 [128, 128, 2] 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] 6 -1 3 625152 models.common.C3 [256, 256, 3] 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] 8 -1 1 1182720 models.common.C3 [512, 512, 1] 9 -1 1 656896 models.common.SPPF [512, 512, 5] 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 1 361984 models.common.C3 [512, 256, 1, False] 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 1 90880 models.common.C3 [256, 128, 1, False] 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 1 296448 models.common.C3 [256, 256, 1, False] 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] 24 [17, 20, 23] 1 37758 models.yolo.Detect [9, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]] Model summary: 214 layers, 7043902 parameters, 7043902 gradients, 16.0 GFLOPs Transferred 343/349 items from yolov5s.pt AMP: checks passed ✅ optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8)) train: Scanning /content/drive/MyDrive/MiniProject/yolov5/taco/train/labels... 1200 images, 1 backgrounds, 0 corrupt: 100% 1200/1200 [00:03<00:00, 320.10it/s] train: New cache created: /content/drive/MyDrive/MiniProject/yolov5/taco/train/labels.cache val: Scanning /content/drive/MyDrive/MiniProject/yolov5/taco/val/labels... 150 images, 1 backgrounds, 0 corrupt: 100% 150/150 [00:00<00:00, 151.36it/s] val: New cache created: /content/drive/MyDrive/MiniProject/yolov5/taco/val/labels.cache AutoAnchor: 4.23 anchors/target, 0.984 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅ Plotting labels to runs/train/exp/labels.jpg... Image sizes 640 train, 640 val Using 4 dataloader workers Logging results to runs/train/exp Starting training for 10 epochs... Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0/9 3.76G 0.1225 0.03524 0.06476 54 640: 16% 12/75 [00:47<01:58, 1.89s/it]
In [ ]:
In [ ]:
print (f"Time Elapsed: {end-start}")
Time Elapsed: 3113.0753841400146
See results
In [ ]:
%cd /content/yolov5/runs/train/exp3/
/content/yolov5/runs/train/exp3
In [ ]:
import matplotlib.pyplot as plt
res_path = 'results.png'
img = plt.imread(res_path)
plt.figure(figsize=(20, 20))
plt.imshow(img)
plt.xticks([])
plt.yticks([])
plt.show()
In [ ]:
import pandas as pd
pd.read_csv('results.csv')
Out[ ]:
| epoch | train/box_loss | train/obj_loss | train/cls_loss | metrics/precision | ... | val/obj_loss | val/cls_loss | x/lr0 | x/lr1 | x/lr2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0.104530 | 0.035835 | 0.059713 | 0.52692 | ... | 0.026340 | 0.053226 | 0.070400 | 0.003289 | 0.003289 |
| 1 | 1 | 0.074191 | 0.033781 | 0.049640 | 0.31868 | ... | 0.021053 | 0.047766 | 0.039089 | 0.005311 | 0.005311 |
| 2 | 2 | 0.064907 | 0.030058 | 0.046801 | 0.62166 | ... | 0.019943 | 0.046435 | 0.006458 | 0.006013 | 0.006013 |
| 3 | 3 | 0.055808 | 0.028955 | 0.046160 | 0.61846 | ... | 0.020071 | 0.045666 | 0.004060 | 0.004060 | 0.004060 |
| 4 | 4 | 0.053244 | 0.029055 | 0.044466 | 0.61693 | ... | 0.019931 | 0.045358 | 0.004060 | 0.004060 | 0.004060 |
5 rows × 14 columns
In [ ]:
!cat /content/yolov5/runs/train/exp3/results.csv
epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
0, 0.10453, 0.035835, 0.059713, 0.52692, 0.090024, 0.021278, 0.0057413, 0.087463, 0.02634, 0.053226, 0.0704, 0.0032889, 0.0032889
1, 0.074191, 0.033781, 0.04964, 0.31868, 0.14579, 0.072828, 0.036463, 0.071678, 0.021053, 0.047766, 0.039089, 0.005311, 0.005311
2, 0.064907, 0.030058, 0.046801, 0.62166, 0.13176, 0.093548, 0.044356, 0.062293, 0.019943, 0.046435, 0.0064576, 0.0060132, 0.0060132
3, 0.055808, 0.028955, 0.04616, 0.61846, 0.17549, 0.12195, 0.06479, 0.051539, 0.020071, 0.045666, 0.00406, 0.00406, 0.00406
4, 0.053244, 0.029055, 0.044466, 0.61693, 0.17788, 0.12095, 0.06385, 0.048399, 0.019931, 0.045358, 0.00406, 0.00406, 0.00406
In [ ]:
import os
os.chdir(r'/content/yolov5/runs/train/exp3')
from IPython.display import FileLink
FileLink(r'results.csv')
Out[ ]:
In [ ]:
%cd /content/yolov5
/content/yolov5
In [ ]:
# !cp runs/exp/weights/best.pt weights
!python detect.py --weights runs/train/exp3/weights/best.pt --img 640 --conf 0.3 --source taco/test/images
detect: weights=['runs/train/exp3/weights/best.pt'], source=taco/test/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1 YOLOv5 🚀 v7.0-46-g96a71b1 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (A100-SXM4-40GB, 40536MiB) Fusing layers... Model summary: 157 layers, 7034398 parameters, 0 gradients, 15.8 GFLOPs image 1/150 /content/yolov5/taco/test/images/batch_1-000029.jpg: 640x480 (no detections), 16.4ms image 2/150 /content/yolov5/taco/test/images/batch_1-000045.jpg: 480x640 2 papers, 16.7ms image 3/150 /content/yolov5/taco/test/images/batch_1-000047.jpg: 480x640 2 plastics, 4 papers, 10.8ms image 4/150 /content/yolov5/taco/test/images/batch_1-000065.JPG: 480x640 2 plastics, 2 papers, 11.0ms image 5/150 /content/yolov5/taco/test/images/batch_1-000108.JPG: 640x480 1 plastic, 11.3ms image 6/150 /content/yolov5/taco/test/images/batch_1-000111.JPG: 640x480 2 plastics, 10.8ms image 7/150 /content/yolov5/taco/test/images/batch_1-000119.JPG: 640x480 1 plastic, 10.8ms image 8/150 /content/yolov5/taco/test/images/batch_10-000002.jpg: 320x640 1 plastic, 16.7ms image 9/150 /content/yolov5/taco/test/images/batch_10-000008.jpg: 640x320 3 plastics, 16.5ms image 10/150 /content/yolov5/taco/test/images/batch_10-000009.jpg: 320x640 (no detections), 11.4ms image 11/150 /content/yolov5/taco/test/images/batch_10-000013.jpg: 320x640 (no detections), 10.9ms image 12/150 /content/yolov5/taco/test/images/batch_10-000019.jpg: 640x320 1 plastic, 11.3ms image 13/150 /content/yolov5/taco/test/images/batch_10-000032.jpg: 640x320 (no detections), 11.1ms image 14/150 /content/yolov5/taco/test/images/batch_10-000035.jpg: 640x320 (no detections), 10.8ms image 15/150 /content/yolov5/taco/test/images/batch_10-000036.jpg: 640x320 1 plastic, 10.8ms image 16/150 /content/yolov5/taco/test/images/batch_10-000039.jpg: 640x320 1 plastic, 10.9ms image 17/150 /content/yolov5/taco/test/images/batch_10-000046.jpg: 640x320 1 plastic, 11.1ms image 18/150 /content/yolov5/taco/test/images/batch_10-000059.jpg: 640x320 2 plastics, 10.9ms image 19/150 /content/yolov5/taco/test/images/batch_11-000013.jpg: 640x320 1 plastic, 10.9ms image 20/150 /content/yolov5/taco/test/images/batch_11-000022.jpg: 448x640 (no detections), 17.2ms image 21/150 /content/yolov5/taco/test/images/batch_11-000041.jpg: 640x480 1 plastic, 11.3ms image 22/150 /content/yolov5/taco/test/images/batch_11-000066.jpg: 640x480 1 plastic, 10.9ms image 23/150 /content/yolov5/taco/test/images/batch_11-000077.jpg: 640x480 1 plastic, 10.8ms image 24/150 /content/yolov5/taco/test/images/batch_12-000006.jpg: 640x384 1 plastic, 17.1ms image 25/150 /content/yolov5/taco/test/images/batch_12-000017.jpg: 640x384 2 plastics, 11.2ms image 26/150 /content/yolov5/taco/test/images/batch_12-000026.jpg: 640x384 2 plastics, 11.1ms image 27/150 /content/yolov5/taco/test/images/batch_12-000038.jpg: 640x480 1 plastic, 11.3ms image 28/150 /content/yolov5/taco/test/images/batch_12-000047.jpg: 640x480 (no detections), 10.7ms image 29/150 /content/yolov5/taco/test/images/batch_12-000049.jpg: 640x480 1 plastic, 11.1ms image 30/150 /content/yolov5/taco/test/images/batch_12-000069.jpg: 640x384 1 plastic, 11.6ms image 31/150 /content/yolov5/taco/test/images/batch_12-000077.jpg: 640x480 2 plastics, 11.4ms image 32/150 /content/yolov5/taco/test/images/batch_12-000091.jpg: 480x640 3 plastics, 11.3ms image 33/150 /content/yolov5/taco/test/images/batch_12-000097.jpg: 640x480 1 plastic, 11.5ms image 34/150 /content/yolov5/taco/test/images/batch_13-000001.jpg: 640x480 (no detections), 11.2ms image 35/150 /content/yolov5/taco/test/images/batch_13-000002.jpg: 480x640 1 plastic, 11.3ms image 36/150 /content/yolov5/taco/test/images/batch_13-000010.jpg: 480x640 2 plastics, 10.9ms image 37/150 /content/yolov5/taco/test/images/batch_13-000018.jpg: 640x480 1 plastic, 11.2ms image 38/150 /content/yolov5/taco/test/images/batch_13-000025.jpg: 640x480 3 plastics, 10.9ms image 39/150 /content/yolov5/taco/test/images/batch_13-000036.jpg: 480x640 (no detections), 11.2ms image 40/150 /content/yolov5/taco/test/images/batch_13-000052.jpg: 480x640 1 plastic, 10.7ms image 41/150 /content/yolov5/taco/test/images/batch_13-000067.jpg: 480x640 (no detections), 10.8ms image 42/150 /content/yolov5/taco/test/images/batch_14-000003.jpg: 480x640 (no detections), 10.9ms image 43/150 /content/yolov5/taco/test/images/batch_14-000016.jpg: 480x640 1 plastic, 11.9ms image 44/150 /content/yolov5/taco/test/images/batch_14-000017.jpg: 480x640 (no detections), 10.9ms image 45/150 /content/yolov5/taco/test/images/batch_14-000025.jpg: 640x480 1 plastic, 11.3ms image 46/150 /content/yolov5/taco/test/images/batch_14-000035.jpg: 640x480 (no detections), 10.7ms image 47/150 /content/yolov5/taco/test/images/batch_14-000038.jpg: 640x480 3 plastics, 10.8ms image 48/150 /content/yolov5/taco/test/images/batch_14-000041.jpg: 640x480 (no detections), 10.8ms image 49/150 /content/yolov5/taco/test/images/batch_14-000082.jpg: 640x480 (no detections), 10.9ms image 50/150 /content/yolov5/taco/test/images/batch_14-000087.jpg: 640x480 1 plastic, 11.0ms image 51/150 /content/yolov5/taco/test/images/batch_15-000028.jpg: 640x480 (no detections), 10.8ms image 52/150 /content/yolov5/taco/test/images/batch_15-000029.jpg: 640x480 (no detections), 12.7ms image 53/150 /content/yolov5/taco/test/images/batch_15-000035.jpg: 640x480 1 plastic, 1 paper, 10.8ms image 54/150 /content/yolov5/taco/test/images/batch_15-000041.jpg: 640x480 1 plastic, 11.2ms image 55/150 /content/yolov5/taco/test/images/batch_15-000042.jpg: 640x480 1 plastic, 10.8ms image 56/150 /content/yolov5/taco/test/images/batch_15-000046.jpg: 480x640 1 plastic, 11.4ms image 57/150 /content/yolov5/taco/test/images/batch_15-000080.jpg: 640x480 (no detections), 11.3ms image 58/150 /content/yolov5/taco/test/images/batch_15-000082.jpg: 640x480 1 plastic, 10.7ms image 59/150 /content/yolov5/taco/test/images/batch_2-000029.JPG: 640x480 2 plastics, 10.7ms image 60/150 /content/yolov5/taco/test/images/batch_2-000030.JPG: 640x448 2 plastics, 16.6ms image 61/150 /content/yolov5/taco/test/images/batch_2-000033.JPG: 640x480 1 plastic, 11.2ms image 62/150 /content/yolov5/taco/test/images/batch_2-000039.JPG: 640x480 1 plastic, 10.7ms image 63/150 /content/yolov5/taco/test/images/batch_2-000040.JPG: 640x480 (no detections), 10.9ms image 64/150 /content/yolov5/taco/test/images/batch_2-000041.JPG: 640x480 1 plastic, 10.9ms image 65/150 /content/yolov5/taco/test/images/batch_2-000055.JPG: 640x480 2 plastics, 10.8ms image 66/150 /content/yolov5/taco/test/images/batch_2-000067.JPG: 480x640 1 plastic, 11.2ms image 67/150 /content/yolov5/taco/test/images/batch_2-000069.JPG: 480x640 1 paper, 10.8ms image 68/150 /content/yolov5/taco/test/images/batch_3-IMG_4854.JPG: 480x640 1 paper, 10.8ms image 69/150 /content/yolov5/taco/test/images/batch_3-IMG_4862.JPG: 640x480 2 papers, 11.4ms image 70/150 /content/yolov5/taco/test/images/batch_3-IMG_4895.JPG: 640x480 1 plastic, 10.7ms image 71/150 /content/yolov5/taco/test/images/batch_3-IMG_4897.JPG: 640x480 1 plastic, 10.8ms image 72/150 /content/yolov5/taco/test/images/batch_3-IMG_4901.JPG: 640x480 (no detections), 11.0ms image 73/150 /content/yolov5/taco/test/images/batch_3-IMG_4924.JPG: 640x480 (no detections), 10.8ms image 74/150 /content/yolov5/taco/test/images/batch_3-IMG_4950.JPG: 640x608 1 plastic, 1 paper, 17.1ms image 75/150 /content/yolov5/taco/test/images/batch_3-IMG_4971.JPG: 640x480 1 plastic, 11.2ms image 76/150 /content/yolov5/taco/test/images/batch_3-IMG_5065.JPG: 480x640 1 paper, 11.2ms image 77/150 /content/yolov5/taco/test/images/batch_3-IMG_5068.JPG: 480x640 5 papers, 11.0ms image 78/150 /content/yolov5/taco/test/images/batch_4-000005.JPG: 480x640 (no detections), 10.8ms image 79/150 /content/yolov5/taco/test/images/batch_4-000006.JPG: 640x480 1 plastic, 11.2ms image 80/150 /content/yolov5/taco/test/images/batch_4-000011.JPG: 640x480 2 plastics, 17.3ms image 81/150 /content/yolov5/taco/test/images/batch_4-000028.JPG: 480x640 (no detections), 11.3ms image 82/150 /content/yolov5/taco/test/images/batch_4-000058.JPG: 480x640 1 plastic, 10.8ms image 83/150 /content/yolov5/taco/test/images/batch_4-000064.JPG: 480x640 (no detections), 10.9ms image 84/150 /content/yolov5/taco/test/images/batch_4-000068.JPG: 640x480 (no detections), 11.1ms image 85/150 /content/yolov5/taco/test/images/batch_4-000076.JPG: 480x640 1 plastic, 11.8ms image 86/150 /content/yolov5/taco/test/images/batch_4-000077.JPG: 480x640 (no detections), 11.0ms image 87/150 /content/yolov5/taco/test/images/batch_5-000028.JPG: 640x480 1 plastic, 11.2ms image 88/150 /content/yolov5/taco/test/images/batch_5-000036.JPG: 640x480 1 plastic, 10.8ms image 89/150 /content/yolov5/taco/test/images/batch_5-000052.JPG: 640x480 (no detections), 10.8ms image 90/150 /content/yolov5/taco/test/images/batch_5-000076.JPG: 640x480 1 plastic, 10.7ms image 91/150 /content/yolov5/taco/test/images/batch_5-000114.JPG: 640x480 2 plastics, 10.8ms image 92/150 /content/yolov5/taco/test/images/batch_6-000000.JPG: 640x480 (no detections), 10.9ms image 93/150 /content/yolov5/taco/test/images/batch_6-000002.JPG: 480x640 1 plastic, 1 paper, 11.3ms image 94/150 /content/yolov5/taco/test/images/batch_6-000007.JPG: 640x480 1 plastic, 1 paper, 11.2ms image 95/150 /content/yolov5/taco/test/images/batch_6-000008.JPG: 480x640 1 plastic, 11.2ms image 96/150 /content/yolov5/taco/test/images/batch_6-000019.JPG: 480x640 3 plastics, 10.8ms image 97/150 /content/yolov5/taco/test/images/batch_6-000034.JPG: 640x480 2 plastics, 11.2ms image 98/150 /content/yolov5/taco/test/images/batch_6-000038.JPG: 480x640 (no detections), 11.3ms image 99/150 /content/yolov5/taco/test/images/batch_6-000050.JPG: 640x480 (no detections), 11.2ms image 100/150 /content/yolov5/taco/test/images/batch_6-000054.JPG: 480x640 2 plastics, 11.2ms image 101/150 /content/yolov5/taco/test/images/batch_6-000061.JPG: 640x480 2 papers, 11.3ms image 102/150 /content/yolov5/taco/test/images/batch_6-000064.JPG: 480x640 (no detections), 11.3ms image 103/150 /content/yolov5/taco/test/images/batch_6-000066.JPG: 480x640 2 plastics, 10.8ms image 104/150 /content/yolov5/taco/test/images/batch_6-000073.JPG: 480x640 (no detections), 12.5ms image 105/150 /content/yolov5/taco/test/images/batch_6-000096.JPG: 640x480 1 paper, 11.3ms image 106/150 /content/yolov5/taco/test/images/batch_6-000097.JPG: 480x640 1 plastic, 11.2ms image 107/150 /content/yolov5/taco/test/images/batch_6-000101.JPG: 480x640 (no detections), 10.8ms image 108/150 /content/yolov5/taco/test/images/batch_6-000103.JPG: 480x640 1 plastic, 10.9ms image 109/150 /content/yolov5/taco/test/images/batch_7-000011.JPG: 448x640 (no detections), 11.3ms image 110/150 /content/yolov5/taco/test/images/batch_7-000024.JPG: 480x640 1 plastic, 11.3ms image 111/150 /content/yolov5/taco/test/images/batch_7-000031.JPG: 640x480 1 plastic, 11.2ms image 112/150 /content/yolov5/taco/test/images/batch_7-000053.JPG: 480x640 1 plastic, 11.2ms image 113/150 /content/yolov5/taco/test/images/batch_7-000068.JPG: 480x640 1 plastic, 10.7ms image 114/150 /content/yolov5/taco/test/images/batch_7-000073.JPG: 640x480 1 plastic, 11.3ms image 115/150 /content/yolov5/taco/test/images/batch_7-000077.JPG: 640x480 2 plastics, 10.8ms image 116/150 /content/yolov5/taco/test/images/batch_7-000080.JPG: 480x640 1 plastic, 11.4ms image 117/150 /content/yolov5/taco/test/images/batch_7-000089.JPG: 480x640 (no detections), 10.8ms image 118/150 /content/yolov5/taco/test/images/batch_7-000091.JPG: 480x640 1 plastic, 10.8ms image 119/150 /content/yolov5/taco/test/images/batch_7-000094.JPG: 640x480 1 plastic, 11.2ms image 120/150 /content/yolov5/taco/test/images/batch_7-000106.JPG: 480x640 1 plastic, 11.2ms image 121/150 /content/yolov5/taco/test/images/batch_7-000108.JPG: 480x640 1 plastic, 10.9ms image 122/150 /content/yolov5/taco/test/images/batch_7-000110.JPG: 640x480 (no detections), 11.1ms image 123/150 /content/yolov5/taco/test/images/batch_7-000120.JPG: 480x640 (no detections), 11.3ms image 124/150 /content/yolov5/taco/test/images/batch_7-000121.JPG: 480x640 1 plastic, 10.8ms image 125/150 /content/yolov5/taco/test/images/batch_7-000141.JPG: 640x480 2 plastics, 12.1ms image 126/150 /content/yolov5/taco/test/images/batch_8-000000.jpg: 480x640 1 plastic, 11.3ms image 127/150 /content/yolov5/taco/test/images/batch_8-000007.jpg: 640x480 1 plastic, 11.8ms image 128/150 /content/yolov5/taco/test/images/batch_8-000018.jpg: 640x480 1 plastic, 10.7ms image 129/150 /content/yolov5/taco/test/images/batch_8-000020.jpg: 640x480 (no detections), 10.7ms image 130/150 /content/yolov5/taco/test/images/batch_8-000022.jpg: 640x480 (no detections), 10.7ms image 131/150 /content/yolov5/taco/test/images/batch_8-000037.jpg: 640x480 1 plastic, 10.8ms image 132/150 /content/yolov5/taco/test/images/batch_8-000039.jpg: 640x480 2 plastics, 10.8ms image 133/150 /content/yolov5/taco/test/images/batch_8-000044.jpg: 480x640 3 plastics, 1 paper, 11.4ms image 134/150 /content/yolov5/taco/test/images/batch_8-000045.jpg: 480x640 (no detections), 10.9ms image 135/150 /content/yolov5/taco/test/images/batch_8-000057.jpg: 640x480 1 plastic, 11.2ms image 136/150 /content/yolov5/taco/test/images/batch_8-000059.jpg: 640x480 (no detections), 10.5ms image 137/150 /content/yolov5/taco/test/images/batch_8-000068.jpg: 640x480 1 trash, 1 plastic, 10.7ms image 138/150 /content/yolov5/taco/test/images/batch_8-000083.jpg: 640x384 (no detections), 11.7ms image 139/150 /content/yolov5/taco/test/images/batch_8-000091.jpg: 640x384 (no detections), 10.9ms image 140/150 /content/yolov5/taco/test/images/batch_9-000014.jpg: 480x640 2 plastics, 11.3ms image 141/150 /content/yolov5/taco/test/images/batch_9-000020.jpg: 640x480 1 plastic, 11.2ms image 142/150 /content/yolov5/taco/test/images/batch_9-000027.jpg: 640x480 (no detections), 10.6ms image 143/150 /content/yolov5/taco/test/images/batch_9-000029.jpg: 640x480 1 trash, 3 plastics, 13.9ms image 144/150 /content/yolov5/taco/test/images/batch_9-000031.jpg: 640x480 (no detections), 10.6ms image 145/150 /content/yolov5/taco/test/images/batch_9-000039.jpg: 640x480 (no detections), 10.7ms image 146/150 /content/yolov5/taco/test/images/batch_9-000053.jpg: 480x640 2 plastics, 11.5ms image 147/150 /content/yolov5/taco/test/images/batch_9-000061.jpg: 640x480 1 plastic, 11.6ms image 148/150 /content/yolov5/taco/test/images/batch_9-000073.jpg: 640x320 2 plastics, 12.2ms image 149/150 /content/yolov5/taco/test/images/batch_9-000080.jpg: 640x320 2 plastics, 10.9ms image 150/150 /content/yolov5/taco/test/images/batch_9-000090.jpg: 640x320 1 plastic, 10.9ms Speed: 0.6ms pre-process, 11.4ms inference, 0.9ms NMS per image at shape (1, 3, 640, 640) Results saved to runs/detect/exp
In [ ]:
!pwd
/content/yolov5
In [ ]:
!ls runs/detect/exp
batch_10-000002.jpg batch_15-000028.jpg batch_6-000061.JPG batch_10-000008.jpg batch_15-000029.jpg batch_6-000064.JPG batch_10-000009.jpg batch_15-000035.jpg batch_6-000066.JPG batch_10-000013.jpg batch_15-000041.jpg batch_6-000073.JPG batch_10-000019.jpg batch_15-000042.jpg batch_6-000096.JPG batch_10-000032.jpg batch_15-000046.jpg batch_6-000097.JPG batch_10-000035.jpg batch_15-000080.jpg batch_6-000101.JPG batch_10-000036.jpg batch_15-000082.jpg batch_6-000103.JPG batch_10-000039.jpg batch_2-000029.JPG batch_7-000011.JPG batch_10-000046.jpg batch_2-000030.JPG batch_7-000024.JPG batch_10-000059.jpg batch_2-000033.JPG batch_7-000031.JPG batch_1-000029.jpg batch_2-000039.JPG batch_7-000053.JPG batch_1-000045.jpg batch_2-000040.JPG batch_7-000068.JPG batch_1-000047.jpg batch_2-000041.JPG batch_7-000073.JPG batch_1-000065.JPG batch_2-000055.JPG batch_7-000077.JPG batch_1-000108.JPG batch_2-000067.JPG batch_7-000080.JPG batch_1-000111.JPG batch_2-000069.JPG batch_7-000089.JPG batch_1-000119.JPG batch_3-IMG_4854.JPG batch_7-000091.JPG batch_11-000013.jpg batch_3-IMG_4862.JPG batch_7-000094.JPG batch_11-000022.jpg batch_3-IMG_4895.JPG batch_7-000106.JPG batch_11-000041.jpg batch_3-IMG_4897.JPG batch_7-000108.JPG batch_11-000066.jpg batch_3-IMG_4901.JPG batch_7-000110.JPG batch_11-000077.jpg batch_3-IMG_4924.JPG batch_7-000120.JPG batch_12-000006.jpg batch_3-IMG_4950.JPG batch_7-000121.JPG batch_12-000017.jpg batch_3-IMG_4971.JPG batch_7-000141.JPG batch_12-000026.jpg batch_3-IMG_5065.JPG batch_8-000000.jpg batch_12-000038.jpg batch_3-IMG_5068.JPG batch_8-000007.jpg batch_12-000047.jpg batch_4-000005.JPG batch_8-000018.jpg batch_12-000049.jpg batch_4-000006.JPG batch_8-000020.jpg batch_12-000069.jpg batch_4-000011.JPG batch_8-000022.jpg batch_12-000077.jpg batch_4-000028.JPG batch_8-000037.jpg batch_12-000091.jpg batch_4-000058.JPG batch_8-000039.jpg batch_12-000097.jpg batch_4-000064.JPG batch_8-000044.jpg batch_13-000001.jpg batch_4-000068.JPG batch_8-000045.jpg batch_13-000002.jpg batch_4-000076.JPG batch_8-000057.jpg batch_13-000010.jpg batch_4-000077.JPG batch_8-000059.jpg batch_13-000018.jpg batch_5-000028.JPG batch_8-000068.jpg batch_13-000025.jpg batch_5-000036.JPG batch_8-000083.jpg batch_13-000036.jpg batch_5-000052.JPG batch_8-000091.jpg batch_13-000052.jpg batch_5-000076.JPG batch_9-000014.jpg batch_13-000067.jpg batch_5-000114.JPG batch_9-000020.jpg batch_14-000003.jpg batch_6-000000.JPG batch_9-000027.jpg batch_14-000016.jpg batch_6-000002.JPG batch_9-000029.jpg batch_14-000017.jpg batch_6-000007.JPG batch_9-000031.jpg batch_14-000025.jpg batch_6-000008.JPG batch_9-000039.jpg batch_14-000035.jpg batch_6-000019.JPG batch_9-000053.jpg batch_14-000038.jpg batch_6-000034.JPG batch_9-000061.jpg batch_14-000041.jpg batch_6-000038.JPG batch_9-000073.jpg batch_14-000082.jpg batch_6-000050.JPG batch_9-000080.jpg batch_14-000087.jpg batch_6-000054.JPG batch_9-000090.jpg
In [ ]:
import glob
from IPython.display import Image, display
def inspectPerformance(mode, run):
cnt = 0
%cd yolov5
for imageName in glob.glob('runs/{}/{}/*.jpg'.format(mode,run)): #assuming JPG
display(Image(filename=imageName))
print("\n")
cnt += 1
if cnt == 5:
break
In [ ]:
# we use the helper function defined above to view the TEST results from EXP __ (we can see this from the last line in the
# log output after running the test command)
inspectPerformance("detect", "exp") # change 'exp19' to the name of your test_run
[Errno 2] No such file or directory: 'yolov5' /content/yolov5
In [ ]:
!python test.py
python: can't open file 'test.py': [Errno 2] No such file or directory
Show Training Result
In [ ]:
%cd '/content/yolov5/runs/train/exp'
%ls
/kaggle/working/yolov5/runs/train/exp
F1_curve.png results.png
PR_curve.png train_batch0.jpg
P_curve.png train_batch1.jpg
R_curve.png train_batch2.jpg
confusion_matrix.png val_batch0_labels.jpg
events.out.tfevents.1648357698.0d1bcf56e556.213.0 val_batch0_pred.jpg
hyp.yaml val_batch1_labels.jpg
labels.jpg val_batch1_pred.jpg
labels_correlogram.jpg val_batch2_labels.jpg
opt.yaml val_batch2_pred.jpg
results.csv weights/
In [ ]:
inspectPerformance("train", "exp")
[Errno 2] No such file or directory: 'yolov5' /kaggle/working/yolov5/runs/train/exp
In [ ]:
!python detect.py --weights runs/train/exp3/weights/best.pt --img 640 --conf 0.3 --source /content/쓰레기.mp4
detect: weights=['runs/train/exp3/weights/best.pt'], source=/content/쓰레기.mp4, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1 YOLOv5 🚀 v7.0-46-g96a71b1 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (A100-SXM4-40GB, 40536MiB) Fusing layers... Model summary: 157 layers, 7034398 parameters, 0 gradients, 15.8 GFLOPs video 1/1 (1/124) /content/쓰레기.mp4: 384x640 3 papers, 17.8ms video 1/1 (2/124) /content/쓰레기.mp4: 384x640 3 papers, 11.1ms video 1/1 (3/124) /content/쓰레기.mp4: 384x640 3 papers, 11.4ms video 1/1 (4/124) /content/쓰레기.mp4: 384x640 2 papers, 11.5ms video 1/1 (5/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms video 1/1 (6/124) /content/쓰레기.mp4: 384x640 3 papers, 11.5ms video 1/1 (7/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms video 1/1 (8/124) /content/쓰레기.mp4: 384x640 3 papers, 11.2ms video 1/1 (9/124) /content/쓰레기.mp4: 384x640 2 papers, 11.2ms video 1/1 (10/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms video 1/1 (11/124) /content/쓰레기.mp4: 384x640 3 papers, 11.2ms video 1/1 (12/124) /content/쓰레기.mp4: 384x640 3 papers, 11.4ms video 1/1 (13/124) /content/쓰레기.mp4: 384x640 3 papers, 11.6ms video 1/1 (14/124) /content/쓰레기.mp4: 384x640 3 papers, 11.8ms video 1/1 (15/124) /content/쓰레기.mp4: 384x640 3 papers, 11.5ms video 1/1 (16/124) /content/쓰레기.mp4: 384x640 4 papers, 11.2ms video 1/1 (17/124) /content/쓰레기.mp4: 384x640 3 papers, 11.6ms video 1/1 (18/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms video 1/1 (19/124) /content/쓰레기.mp4: 384x640 3 papers, 11.1ms video 1/1 (20/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms video 1/1 (21/124) /content/쓰레기.mp4: 384x640 4 papers, 11.3ms video 1/1 (22/124) /content/쓰레기.mp4: 384x640 4 papers, 11.2ms video 1/1 (23/124) /content/쓰레기.mp4: 384x640 3 papers, 11.2ms video 1/1 (24/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms video 1/1 (25/124) /content/쓰레기.mp4: 384x640 3 papers, 11.1ms video 1/1 (26/124) /content/쓰레기.mp4: 384x640 3 papers, 11.4ms video 1/1 (27/124) /content/쓰레기.mp4: 384x640 3 papers, 11.0ms video 1/1 (28/124) /content/쓰레기.mp4: 384x640 3 papers, 11.4ms video 1/1 (29/124) /content/쓰레기.mp4: 384x640 4 papers, 11.2ms video 1/1 (30/124) /content/쓰레기.mp4: 384x640 3 papers, 11.2ms video 1/1 (31/124) /content/쓰레기.mp4: 384x640 4 papers, 11.1ms video 1/1 (32/124) /content/쓰레기.mp4: 384x640 4 papers, 11.1ms video 1/1 (33/124) /content/쓰레기.mp4: 384x640 5 papers, 11.2ms video 1/1 (34/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.2ms video 1/1 (35/124) /content/쓰레기.mp4: 384x640 4 papers, 11.3ms video 1/1 (36/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.3ms video 1/1 (37/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.3ms video 1/1 (38/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.4ms video 1/1 (39/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.7ms video 1/1 (40/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.2ms video 1/1 (41/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.5ms video 1/1 (42/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 12.2ms video 1/1 (43/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.2ms video 1/1 (44/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.3ms video 1/1 (45/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.3ms video 1/1 (46/124) /content/쓰레기.mp4: 384x640 2 plastics, 2 papers, 11.3ms video 1/1 (47/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms video 1/1 (48/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.0ms video 1/1 (49/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.5ms video 1/1 (50/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.1ms video 1/1 (51/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.1ms video 1/1 (52/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.3ms video 1/1 (53/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.3ms video 1/1 (54/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.4ms video 1/1 (55/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.2ms video 1/1 (56/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.3ms video 1/1 (57/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.4ms video 1/1 (58/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.7ms video 1/1 (59/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms video 1/1 (60/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms video 1/1 (61/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms video 1/1 (62/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms video 1/1 (63/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms video 1/1 (64/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.3ms video 1/1 (65/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.0ms video 1/1 (66/124) /content/쓰레기.mp4: 384x640 2 plastics, 2 papers, 11.2ms video 1/1 (67/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.1ms video 1/1 (68/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms video 1/1 (69/124) /content/쓰레기.mp4: 384x640 3 plastics, 3 papers, 11.1ms video 1/1 (70/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms video 1/1 (71/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.1ms video 1/1 (72/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms video 1/1 (73/124) /content/쓰레기.mp4: 384x640 2 plastics, 2 papers, 11.1ms video 1/1 (74/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 10.9ms video 1/1 (75/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms video 1/1 (76/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.2ms video 1/1 (77/124) /content/쓰레기.mp4: 384x640 3 plastics, 4 papers, 10.9ms video 1/1 (78/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.0ms video 1/1 (79/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms video 1/1 (80/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.7ms video 1/1 (81/124) /content/쓰레기.mp4: 384x640 3 plastics, 5 papers, 11.3ms video 1/1 (82/124) /content/쓰레기.mp4: 384x640 2 plastics, 6 papers, 11.2ms video 1/1 (83/124) /content/쓰레기.mp4: 384x640 1 plastic, 6 papers, 12.9ms video 1/1 (84/124) /content/쓰레기.mp4: 384x640 1 plastic, 5 papers, 11.3ms video 1/1 (85/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.8ms video 1/1 (86/124) /content/쓰레기.mp4: 384x640 1 plastic, 5 papers, 12.3ms video 1/1 (87/124) /content/쓰레기.mp4: 384x640 2 plastics, 6 papers, 11.7ms video 1/1 (88/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.2ms video 1/1 (89/124) /content/쓰레기.mp4: 384x640 2 plastics, 6 papers, 13.4ms video 1/1 (90/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 12.7ms video 1/1 (91/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.4ms video 1/1 (92/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.4ms video 1/1 (93/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.4ms video 1/1 (94/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.3ms video 1/1 (95/124) /content/쓰레기.mp4: 384x640 2 plastics, 6 papers, 11.4ms video 1/1 (96/124) /content/쓰레기.mp4: 384x640 6 papers, 11.3ms video 1/1 (97/124) /content/쓰레기.mp4: 384x640 3 plastics, 5 papers, 11.2ms video 1/1 (98/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.2ms video 1/1 (99/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.4ms video 1/1 (100/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms video 1/1 (101/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.2ms video 1/1 (102/124) /content/쓰레기.mp4: 384x640 1 plastic, 5 papers, 11.2ms video 1/1 (103/124) /content/쓰레기.mp4: 384x640 1 plastic, 5 papers, 11.6ms video 1/1 (104/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.5ms video 1/1 (105/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 13.5ms video 1/1 (106/124) /content/쓰레기.mp4: 384x640 1 plastic, 5 papers, 12.5ms video 1/1 (107/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.1ms video 1/1 (108/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.3ms video 1/1 (109/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.2ms video 1/1 (110/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.3ms video 1/1 (111/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.4ms video 1/1 (112/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 12.0ms video 1/1 (113/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms video 1/1 (114/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.7ms video 1/1 (115/124) /content/쓰레기.mp4: 384x640 1 plastic, 2 papers, 11.2ms video 1/1 (116/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.8ms video 1/1 (117/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.3ms video 1/1 (118/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.3ms video 1/1 (119/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.5ms video 1/1 (120/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.5ms video 1/1 (121/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 12.0ms video 1/1 (122/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 10.9ms video 1/1 (123/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 10.9ms video 1/1 (124/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.2ms Speed: 0.5ms pre-process, 11.4ms inference, 1.1ms NMS per image at shape (1, 3, 640, 640) Results saved to runs/detect/exp2
In [ ]:
!python detect.py --weights runs/train/exp3/weights/best.pt --img 640 --conf 0.3 --source /content/쓰레기2.mp4
detect: weights=['runs/train/exp3/weights/best.pt'], source=/content/쓰레기2.mp4, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.3, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1 YOLOv5 🚀 v7.0-46-g96a71b1 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (A100-SXM4-40GB, 40536MiB) Fusing layers... Model summary: 157 layers, 7034398 parameters, 0 gradients, 15.8 GFLOPs video 1/1 (1/168) /content/쓰레기2.mp4: 640x384 1 paper, 17.2ms video 1/1 (2/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.6ms video 1/1 (3/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (4/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms video 1/1 (5/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (6/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (7/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms video 1/1 (8/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.7ms video 1/1 (9/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (10/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.3ms video 1/1 (11/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.2ms video 1/1 (12/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.2ms video 1/1 (13/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.8ms video 1/1 (14/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.5ms video 1/1 (15/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.6ms video 1/1 (16/168) /content/쓰레기2.mp4: 640x384 2 plastics, 1 paper, 10.7ms video 1/1 (17/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.4ms video 1/1 (18/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.5ms video 1/1 (19/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.5ms video 1/1 (20/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (21/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (22/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (23/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (24/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (25/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (26/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (27/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (28/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (29/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms video 1/1 (30/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (31/168) /content/쓰레기2.mp4: 640x384 1 plastic, 10.4ms video 1/1 (32/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms video 1/1 (33/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.8ms video 1/1 (34/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms video 1/1 (35/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms video 1/1 (36/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms video 1/1 (37/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms video 1/1 (38/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms video 1/1 (39/168) /content/쓰레기2.mp4: 640x384 1 plastic, 10.4ms video 1/1 (40/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.7ms video 1/1 (41/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.6ms video 1/1 (42/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.7ms video 1/1 (43/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.6ms video 1/1 (44/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms video 1/1 (45/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.8ms video 1/1 (46/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms video 1/1 (47/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (48/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms video 1/1 (49/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms video 1/1 (50/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (51/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (52/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (53/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (54/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (55/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (56/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms video 1/1 (57/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (58/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (59/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.8ms video 1/1 (60/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (61/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (62/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (63/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (64/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (65/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (66/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (67/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (68/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.2ms video 1/1 (69/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (70/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (71/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (72/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.3ms video 1/1 (73/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (74/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.1ms video 1/1 (75/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.2ms video 1/1 (76/168) /content/쓰레기2.mp4: 640x384 1 paper, 12.0ms video 1/1 (77/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (78/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (79/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (80/168) /content/쓰레기2.mp4: 640x384 1 paper, 11.8ms video 1/1 (81/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (82/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.3ms video 1/1 (83/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (84/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.2ms video 1/1 (85/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms video 1/1 (86/168) /content/쓰레기2.mp4: 640x384 (no detections), 12.1ms video 1/1 (87/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (88/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms video 1/1 (89/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (90/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms video 1/1 (91/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (92/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (93/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (94/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (95/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (96/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (97/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (98/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (99/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (100/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (101/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms video 1/1 (102/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (103/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (104/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (105/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms video 1/1 (106/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (107/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (108/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (109/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (110/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.1ms video 1/1 (111/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (112/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms video 1/1 (113/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (114/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (115/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms video 1/1 (116/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms video 1/1 (117/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (118/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (119/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (120/168) /content/쓰레기2.mp4: 640x384 1 paper, 11.9ms video 1/1 (121/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (122/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (123/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (124/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.6ms video 1/1 (125/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (126/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms video 1/1 (127/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.8ms video 1/1 (128/168) /content/쓰레기2.mp4: 640x384 1 paper, 12.4ms video 1/1 (129/168) /content/쓰레기2.mp4: 640x384 (no detections), 11.7ms video 1/1 (130/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.6ms video 1/1 (131/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.7ms video 1/1 (132/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.8ms video 1/1 (133/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms video 1/1 (134/168) /content/쓰레기2.mp4: 640x384 1 plastic, 10.4ms video 1/1 (135/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (136/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (137/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (138/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (139/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (140/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (141/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms video 1/1 (142/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (143/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (144/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms video 1/1 (145/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms video 1/1 (146/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (147/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (148/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms video 1/1 (149/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms video 1/1 (150/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.7ms video 1/1 (151/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms video 1/1 (152/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.2ms video 1/1 (153/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms video 1/1 (154/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.6ms video 1/1 (155/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (156/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (157/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (158/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms video 1/1 (159/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (160/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.1ms video 1/1 (161/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.1ms video 1/1 (162/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.5ms video 1/1 (163/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.7ms video 1/1 (164/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms video 1/1 (165/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.3ms video 1/1 (166/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.5ms video 1/1 (167/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.4ms video 1/1 (168/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms Speed: 0.4ms pre-process, 10.5ms inference, 0.8ms NMS per image at shape (1, 3, 640, 640) Results saved to runs/detect/exp3
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